Class NearestCentroidScikitsLearnNode

Nearest centroid classifier.
This node has been automatically generated by wrapping the ``sklearn.neighbors.nearest_centroid.NearestCentroid`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Each class is represented by its centroid, with test samples classified to
the class with the nearest centroid.
Read more in the :ref:`User Guide <nearest_centroid_classifier>`.
**Parameters**
metric: string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by metrics.pairwise.pairwise_distances for its
metric parameter.
The centroids for the samples corresponding to each class is the point
from which the sum of the distances (according to the metric) of all
samples that belong to that particular class are minimized.
If the "manhattan" metric is provided, this centroid is the median and
for all other metrics, the centroid is now set to be the mean.
shrink_threshold : float, optional (default = None)
Threshold for shrinking centroids to remove features.
**Attributes**
``centroids_`` : array-like, shape = [n_classes, n_features]
Centroid of each class
**Examples**
>>> from sklearn.neighbors.nearest_centroid import NearestCentroid
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid(metric='euclidean', shrink_threshold=None)
>>> print(clf.predict([[-0.8, -1]]))
[1]
See also
sklearn.neighbors.KNeighborsClassifier: nearest neighbors classifier
**Notes**
When used for text classification with tf-idf vectors, this classifier is
also known as the Rocchio classifier.
**References**
Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of
multiple cancer types by shrunken centroids of gene expression. Proceedings
of the National Academy of Sciences of the United States of America,
99(10), 6567-6572. The National Academy of Sciences.

Nearest centroid classifier.
This node has been automatically generated by wrapping the ``sklearn.neighbors.nearest_centroid.NearestCentroid`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Each class is represented by its centroid, with test samples classified to
the class with the nearest centroid.
Read more in the :ref:`User Guide <nearest_centroid_classifier>`.
**Parameters**
metric: string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by metrics.pairwise.pairwise_distances for its
metric parameter.
The centroids for the samples corresponding to each class is the point
from which the sum of the distances (according to the metric) of all
samples that belong to that particular class are minimized.
If the "manhattan" metric is provided, this centroid is the median and
for all other metrics, the centroid is now set to be the mean.
shrink_threshold : float, optional (default = None)
Threshold for shrinking centroids to remove features.
**Attributes**
``centroids_`` : array-like, shape = [n_classes, n_features]
Centroid of each class
**Examples**
>>> from sklearn.neighbors.nearest_centroid import NearestCentroid
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid(metric='euclidean', shrink_threshold=None)
>>> print(clf.predict([[-0.8, -1]]))
[1]
See also
sklearn.neighbors.KNeighborsClassifier: nearest neighbors classifier
**Notes**
When used for text classification with tf-idf vectors, this classifier is
also known as the Rocchio classifier.
**References**
Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of
multiple cancer types by shrunken centroids of gene expression. Proceedings
of the National Academy of Sciences of the United States of America,
99(10), 6567-6572. The National Academy of Sciences.

Overrides:
object.__init__

_get_supported_dtypes(self)

Return the list of dtypes supported by this node.
The types can be specified in any format allowed by numpy.dtype.

is_trainable()Static Method

label(self,
x)

Perform classification on an array of test vectors X.

This node has been automatically generated by wrapping the sklearn.neighbors.nearest_centroid.NearestCentroid class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

The predicted class C for each sample in X is returned.

Parameters

X : array-like, shape = [n_samples, n_features]

Returns

C : array, shape = [n_samples]

Notes

If the metric constructor parameter is "precomputed", X is assumed to
be the distance matrix between the data to be predicted and
self.centroids_.

stop_training(self,
**kwargs)

Fit the NearestCentroid model according to the given training data.

This node has been automatically generated by wrapping the sklearn.neighbors.nearest_centroid.NearestCentroid class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

Parameters

X :{array-like, sparse matrix}, shape = [n_samples, n_features]

Training vector, where n_samples in the number of samples and
n_features is the number of features.
Note that centroid shrinking cannot be used with sparse matrices.